High-res multi-view results

This table lists the benchmark results for the high-res multi-view scenario. The following metrics are evaluated:

Accuracy [%]: The fraction of the reconstruction which is closer to the ground truth than the evaluation threshold distance (*). Larger is better.
Completeness [%]: The fraction of the ground truth which is closer to the reconstruction than the evaluation threshold distance (*). Larger is better.
F₁ score [%]: The harmonic mean of accuracy and completeness, used to rank methods based on both metrics. Larger is better.
Time [s]: The runtime of the method. Smaller is better.

(*) For exact definitions, detailing how potentially incomplete ground truth is taken into account, see our paper.

The datasets are grouped into different categories, and result averages are computed for a category and method if results of the method are available for all datasets within the category. Note that the category "all" includes both the high-res multi-view and the low-res many-view scenarios.

Methods with suffix _ROB may participate in the Robust Vision Challenge.

Click a dataset result cell to show a visualization of the reconstruction. For training datasets, ground truth and accuracy / completeness visualizations are also available. The visualizations may not work with mobile browsers.

Method	all	high-res multi-view	indoor	outdoor	courty.	delive.	electro	facade	kicker	meadow	office	pipes	playgr.	relief	relief.	terrace	terrai.

DVP-MVS++		98.07 1	98.46 1	97.62 1	99.08 3	99.29 2	98.84 3	96.63 4	97.89 1	95.35 3	97.83 4	99.48 3	96.98 9	97.99 2	97.14 12	98.82 11	99.60 1

SED-MVS		97.98 2	98.36 2	97.54 4	98.94 16	99.29 2	98.81 4	96.64 3	97.14 3	95.16 4	97.96 2	99.61 2	96.92 12	98.00 1	97.24 9	98.75 17	99.25 3

DVP-MVS		97.93 3	98.23 3	97.58 3	99.05 6	99.31 1	98.64 6	96.62 5	97.56 2	95.08 5	96.61 13	99.64 1	97.32 1	97.91 4	97.31 7	98.77 15	99.26 2
Zhenlong Yuan, Jinguo Luo, Fei Shen, Zhaoxin Li, Cong Liu, Tianlu Mao, Zhaoqi Wang: DVP-MVS: Synergize Depth-Edge and Visibility Prior for Multi-View Stereo. AAAI2025
DPE-MVS		97.83 4	98.13 4	97.48 5	98.83 22	99.19 4	98.99 1	96.70 1	96.05 18	94.39 9	98.30 1	99.47 4	97.02 6	97.75 7	97.02 15	98.98 3	99.10 9
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
APDe-MVS		97.79 5	97.96 6	97.60 2	99.20 2	99.10 6	98.88 2	96.68 2	96.81 7	94.64 6	97.84 3	98.02 11	97.24 3	97.74 8	97.02 15	98.97 5	99.16 6
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
MSP-MVS		97.70 6	98.09 5	97.24 6	99.00 11	99.17 5	98.76 5	96.41 9	96.91 5	93.88 14	97.72 5	99.04 7	96.93 11	97.29 18	97.31 7	98.45 37	99.23 4
Zhenlong Yuan, Cong Liu, Fei Shen, Zhaoxin Li, Jingguo luo, Tianlu Mao and Zhaoqi Wang: MSP-MVS: Multi-granularity Segmentation Prior Guided Multi-View Stereo. AAAI2025
SMA-MVS		97.53 7	97.93 7	97.07 10	99.21 1	99.02 9	98.08 19	96.25 11	96.36 12	93.57 15	96.56 14	99.27 5	96.78 16	97.91 4	97.43 4	98.51 26	98.94 12
Yufeng Yin; Xiaoyan Liu; Zichao Zhang: SMA-MVS: Segmentation-Guided Multi-Scale Anchor Deformation Patch Multi-View Stereo. IEEE Transactions on Circuits and Systems for Video Technology
SD-MVS		97.35 8	97.73 8	96.90 14	97.35 45	98.66 15	97.85 26	96.25 11	96.86 6	94.54 8	96.75 11	99.13 6	96.99 7	96.94 27	96.58 24	98.39 44	99.20 5
Zhenlong Yuan, Jiakai Cao, Zhaoxin Li, Hao Jiang and Zhaoqi Wang: SD-MVS: Segmentation-driven Deformation Multi-View Stereo with Spherical Refinement and EM optimization. AAAI2024
TSAR-MVS + MP.		97.31 9	97.64 9	96.92 13	97.28 47	98.56 24	98.61 7	95.48 28	96.72 8	94.03 13	96.73 12	98.29 9	97.15 4	97.61 13	96.42 26	98.96 6	99.13 7
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
CNVR-MVS		97.30 10	97.41 11	97.18 8	99.02 10	98.60 22	98.15 16	96.24 13	96.12 17	94.10 11	95.54 26	97.99 12	96.99 7	97.97 3	97.17 10	98.57 24	98.50 32

HPM-MVS++		97.22 11	97.40 12	97.01 11	99.08 3	98.55 25	98.19 14	96.48 7	96.02 19	93.28 20	96.26 18	98.71 8	96.76 17	97.30 17	96.25 39	98.30 54	98.68 18

SF-MVS		97.20 12	97.29 15	97.10 9	98.95 15	98.51 30	97.51 30	96.48 7	96.17 16	94.64 6	97.32 6	97.57 19	96.23 26	96.78 29	96.15 43	98.79 14	98.55 30

APD-MVS		97.12 13	97.05 19	97.19 7	99.04 7	98.63 20	98.45 8	96.54 6	94.81 37	93.50 16	96.10 20	97.40 22	96.81 13	97.05 23	96.82 20	98.80 12	98.56 25
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
HFP-MVS		97.11 14	97.19 17	97.00 12	98.97 13	98.73 13	98.37 11	95.69 21	96.60 9	93.28 20	96.87 8	96.64 29	97.27 2	96.64 35	96.33 36	98.44 38	98.56 25

SteuartSystems-ACMMP		97.10 15	97.49 10	96.65 18	98.97 13	98.95 10	98.43 9	95.96 17	95.12 29	91.46 29	96.85 9	97.60 18	96.37 24	97.76 6	97.16 11	98.68 19	98.97 11
Skip Steuart: Steuart Systems R&D Blog.
ACMMP_NAP		96.93 16	97.27 16	96.53 23	99.06 5	98.95 10	98.24 13	96.06 15	95.66 22	90.96 33	95.63 25	97.71 16	96.53 20	97.66 11	96.68 21	98.30 54	98.61 23

ACMMPR		96.92 17	96.96 20	96.87 15	98.99 12	98.78 12	98.38 10	95.52 24	96.57 10	92.81 24	96.06 21	95.90 37	97.07 5	96.60 37	96.34 35	98.46 34	98.42 36

MCST-MVS		96.83 18	97.06 18	96.57 19	98.88 20	98.47 32	98.02 21	96.16 14	95.58 24	90.96 33	95.78 24	97.84 14	96.46 22	97.00 26	96.17 41	98.94 7	98.55 30

NCCC		96.75 19	96.67 25	96.85 16	99.03 9	98.44 34	98.15 16	96.28 10	96.32 13	92.39 26	92.16 36	97.55 20	96.68 19	97.32 15	96.65 23	98.55 25	98.26 41

CP-MVS		96.68 20	96.59 27	96.77 17	98.85 21	98.58 23	98.18 15	95.51 26	95.34 26	92.94 23	95.21 29	96.25 31	96.79 15	96.44 42	95.77 51	98.35 46	98.56 25

MP-MVS		96.56 21	96.72 24	96.37 24	98.93 18	98.48 31	98.04 20	95.55 23	94.32 41	90.95 35	95.88 23	97.02 26	96.29 25	96.77 30	96.01 49	98.47 32	98.56 25
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
MVS_0304		96.54 22	97.36 14	95.60 33	98.03 34	99.07 7	98.02 21	92.24 45	95.87 20	92.54 25	96.41 15	96.08 32	94.03 52	97.69 9	97.47 3	98.73 18	98.90 13

DeepC-MVS_fast	93.32 1	96.48 23	96.42 28	96.56 20	98.70 25	98.31 38	97.97 23	95.76 20	96.31 14	92.01 28	91.43 41	95.42 41	96.46 22	97.65 12	97.69 1	98.49 31	98.12 49
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
TSAR-MVS + ACMM		96.19 24	97.39 13	94.78 38	97.70 41	98.41 35	97.72 28	95.49 27	96.47 11	86.66 69	96.35 16	97.85 13	93.99 53	97.19 21	96.37 31	97.12 137	99.13 7

PGM-MVS		96.16 25	96.33 29	95.95 26	99.04 7	98.63 20	98.32 12	92.76 42	93.42 50	90.49 38	96.30 17	95.31 42	96.71 18	96.46 40	96.02 48	98.38 45	98.19 44

train_agg		96.15 26	96.64 26	95.58 34	98.44 27	98.03 48	98.14 18	95.40 31	93.90 47	87.72 60	96.26 18	98.10 10	95.75 31	96.25 47	95.45 57	98.01 88	98.47 34

X-MVS		96.07 27	96.33 29	95.77 29	98.94 16	98.66 15	97.94 24	95.41 30	95.12 29	88.03 55	93.00 34	96.06 33	95.85 29	96.65 34	96.35 32	98.47 32	98.48 33

MSLP-MVS++		96.05 28	95.63 32	96.55 21	98.33 29	98.17 44	96.94 37	94.61 34	94.70 39	94.37 10	89.20 53	95.96 36	96.81 13	95.57 58	97.33 6	98.24 63	98.47 34

TSAR-MVS + GP.		95.86 29	96.95 22	94.60 42	94.07 88	98.11 46	96.30 44	91.76 50	95.67 21	91.07 31	96.82 10	97.69 17	95.71 32	95.96 52	95.75 52	98.68 19	98.63 20

PHI-MVS		95.86 29	96.93 23	94.61 41	97.60 43	98.65 19	96.49 41	93.13 40	94.07 44	87.91 59	97.12 7	97.17 24	93.90 56	96.46 40	96.93 18	98.64 21	98.10 51

CSCG		95.68 31	95.46 36	95.93 27	98.71 24	99.07 7	97.13 35	93.55 37	95.48 25	93.35 19	90.61 46	93.82 47	95.16 37	94.60 82	95.57 55	97.70 110	99.08 10

CPTT-MVS		95.54 32	95.07 38	96.10 25	97.88 37	97.98 50	97.92 25	94.86 32	94.56 40	92.16 27	91.01 42	95.71 38	96.97 10	94.56 83	93.50 95	96.81 161	98.14 47

ACMMP		95.54 32	95.49 35	95.61 32	98.27 31	98.53 27	97.16 34	94.86 32	94.88 35	89.34 44	95.36 28	91.74 55	95.50 35	95.51 59	94.16 78	98.50 29	98.22 42
Qingshan Xu, Weihang Kong, Wenbing Tao, Marc Pollefeys: Multi-Scale Geometric Consistency Guided and Planar Prior Assisted Multi-View Stereo. IEEE Transactions on Pattern Analysis and Machine Intelligence
DeepPCF-MVS	92.65 2	95.50 34	96.96 20	93.79 51	96.44 58	98.21 42	93.51 102	94.08 36	96.94 4	89.29 45	93.08 33	96.77 28	93.82 57	97.68 10	97.40 5	95.59 184	98.65 19

DeepC-MVS	92.10 3	95.22 35	94.77 42	95.75 30	97.77 39	98.54 26	97.63 29	95.96 17	95.07 32	88.85 49	85.35 76	91.85 54	95.82 30	96.88 28	97.10 13	98.44 38	98.63 20
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
DPM-MVS		95.07 36	94.84 41	95.34 35	97.44 44	97.49 68	97.76 27	95.52 24	94.88 35	88.92 48	87.25 62	96.44 30	94.41 44	95.78 55	96.11 45	97.99 90	95.95 132

3Dnovator+	90.56 5	95.06 37	94.56 45	95.65 31	98.11 32	98.15 45	97.19 33	91.59 52	95.11 31	93.23 22	81.99 105	94.71 44	95.43 36	96.48 39	96.88 19	98.35 46	98.63 20

AdaColmap		95.02 38	93.71 50	96.54 22	98.51 26	97.76 59	96.69 40	95.94 19	93.72 49	93.50 16	89.01 54	90.53 66	96.49 21	94.51 85	93.76 88	98.07 81	96.69 102

CANet		94.85 39	94.92 40	94.78 38	97.25 48	98.52 29	97.20 32	91.81 49	93.25 52	91.06 32	86.29 69	94.46 45	92.99 68	97.02 25	96.68 21	98.34 48	98.20 43

MVS_111021_LR		94.84 40	95.57 33	94.00 45	97.11 50	97.72 63	94.88 66	91.16 55	95.24 28	88.74 50	96.03 22	91.52 59	94.33 48	95.96 52	95.01 66	97.79 100	97.49 74

MVS_111021_HR		94.84 40	95.91 31	93.60 52	97.35 45	98.46 33	95.08 62	91.19 54	94.18 43	85.97 77	95.38 27	92.56 52	93.61 60	96.61 36	96.25 39	98.40 42	97.92 58

CDPH-MVS		94.80 42	95.50 34	93.98 47	98.34 28	98.06 47	97.41 31	93.23 39	92.81 55	82.98 106	92.51 35	94.82 43	93.53 61	96.08 50	96.30 38	98.42 40	97.94 56

3Dnovator	90.28 7	94.70 43	94.34 48	95.11 36	98.06 33	98.21 42	96.89 38	91.03 57	94.72 38	91.45 30	82.87 94	93.10 50	94.61 42	96.24 48	97.08 14	98.63 22	98.16 45

SPE-MVS-test		94.63 44	95.28 37	93.88 50	96.56 57	98.67 14	93.41 104	89.31 82	94.27 42	89.64 43	90.84 44	91.64 57	95.58 33	97.04 24	96.17 41	98.77 15	98.32 39

CS-MVS		94.53 45	94.73 43	94.31 43	96.30 60	98.53 27	94.98 63	89.24 84	93.37 51	90.24 40	88.96 55	89.76 71	96.09 28	97.48 14	96.42 26	98.99 2	98.59 24

OMC-MVS		94.49 46	94.36 47	94.64 40	97.17 49	97.73 61	95.49 55	92.25 44	96.18 15	90.34 39	88.51 56	92.88 51	94.90 41	94.92 70	94.17 77	97.69 112	96.15 124

PLC	90.69 4	94.32 47	92.99 58	95.87 28	97.91 35	96.49 97	95.95 51	94.12 35	94.94 33	94.09 12	85.90 72	90.77 63	95.58 33	94.52 84	93.32 102	97.55 120	95.00 154
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
EC-MVSNet		94.19 48	95.05 39	93.18 58	93.56 104	97.65 64	95.34 59	86.37 124	92.05 61	88.71 51	89.91 49	93.32 48	96.14 27	97.29 18	96.42 26	98.98 3	98.70 16

QAPM		94.13 49	94.33 49	93.90 48	97.82 38	98.37 37	96.47 42	90.89 58	92.73 57	85.63 85	85.35 76	93.87 46	94.17 49	95.71 57	95.90 50	98.40 42	98.42 36

EPNet		93.92 50	94.40 46	93.36 54	97.89 36	96.55 95	96.08 47	92.14 46	91.65 67	89.16 46	94.07 31	90.17 70	87.78 132	95.24 64	94.97 67	97.09 139	98.15 46
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
ETV-MVS		93.80 51	94.57 44	92.91 66	93.98 90	97.50 67	93.62 99	88.70 90	91.95 62	87.57 61	90.21 48	90.79 62	94.56 43	97.20 20	96.35 32	99.02 1	97.98 53

DELS-MVS		93.71 52	93.47 52	94.00 45	96.82 54	98.39 36	96.80 39	91.07 56	89.51 103	89.94 42	83.80 86	89.29 72	90.95 94	97.32 15	97.65 2	98.42 40	98.32 39
Christian Sormann, Emanuele Santellani, Mattia Rossi, Andreas Kuhn, Friedrich Fraundorfer: DELS-MVS: Deep Epipolar Line Search for Multi-View Stereo. Winter Conference on Applications of Computer Vision (WACV), 2023
CNLPA		93.69 53	92.50 65	95.06 37	97.11 50	97.36 70	93.88 92	93.30 38	95.64 23	93.44 18	80.32 117	90.73 64	94.99 40	93.58 106	93.33 100	97.67 114	96.57 107

TAPA-MVS	90.35 6	93.69 53	93.52 51	93.90 48	96.89 53	97.62 65	96.15 45	91.67 51	94.94 33	85.97 77	87.72 61	91.96 53	94.40 45	93.76 103	93.06 112	98.30 54	95.58 142
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
sasdasda		93.08 55	93.09 55	93.07 62	94.24 82	97.86 52	95.45 57	87.86 109	94.00 45	87.47 62	88.32 57	82.37 105	95.13 38	93.96 99	96.41 29	98.27 58	98.73 14

canonicalmvs		93.08 55	93.09 55	93.07 62	94.24 82	97.86 52	95.45 57	87.86 109	94.00 45	87.47 62	88.32 57	82.37 105	95.13 38	93.96 99	96.41 29	98.27 58	98.73 14

PCF-MVS	90.19 8	92.98 57	92.07 73	94.04 44	96.39 59	97.87 51	96.03 48	95.47 29	87.16 124	85.09 96	84.81 80	93.21 49	93.46 63	91.98 138	91.98 135	97.78 102	97.51 73
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
PVSNet_BlendedMVS		92.80 58	92.44 67	93.23 55	96.02 62	97.83 56	93.74 96	90.58 59	91.86 64	90.69 36	85.87 74	82.04 110	90.01 106	96.39 43	95.26 60	98.34 48	97.81 63

PVSNet_Blended		92.80 58	92.44 67	93.23 55	96.02 62	97.83 56	93.74 96	90.58 59	91.86 64	90.69 36	85.87 74	82.04 110	90.01 106	96.39 43	95.26 60	98.34 48	97.81 63

MGCFI-Net		92.75 60	92.98 59	92.48 70	94.18 84	97.77 58	95.28 61	87.77 111	93.88 48	85.28 94	88.19 59	82.17 109	94.14 50	93.86 101	96.32 37	98.20 67	98.69 17

EIA-MVS		92.72 61	92.96 60	92.44 72	93.86 97	97.76 59	93.13 108	88.65 93	89.78 99	86.68 68	86.69 66	87.57 73	93.74 58	96.07 51	95.32 58	98.58 23	97.53 72

MAR-MVS		92.71 62	92.63 63	92.79 67	97.70 41	97.15 79	93.75 95	87.98 103	90.71 73	85.76 83	86.28 70	86.38 79	94.35 47	94.95 68	95.49 56	97.22 130	97.44 75
Zhenyu Xu, Yiguang Liu, Xuelei Shi, Ying Wang, Yunan Zheng: MARMVS: Matching Ambiguity Reduced Multiple View Stereo for Efficient Large Scale Scene Reconstruction. CVPR 2020
OpenMVS	88.18 11	92.51 63	91.61 80	93.55 53	97.74 40	98.02 49	95.66 53	90.46 61	89.14 106	86.50 70	75.80 143	90.38 69	92.69 72	94.99 67	95.30 59	98.27 58	97.63 67

CLD-MVS		92.50 64	91.96 75	93.13 59	93.93 94	96.24 103	95.69 52	88.77 89	92.92 53	89.01 47	88.19 59	81.74 113	93.13 66	93.63 105	93.08 110	98.23 64	97.91 60
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
TSAR-MVS + COLMAP		92.39 65	92.31 70	92.47 71	95.35 74	96.46 99	96.13 46	92.04 48	95.33 27	80.11 122	94.95 30	77.35 145	94.05 51	94.49 86	93.08 110	97.15 134	94.53 158

HQP-MVS		92.39 65	92.49 66	92.29 75	95.65 66	95.94 110	95.64 54	92.12 47	92.46 59	79.65 124	91.97 38	82.68 101	92.92 71	93.47 111	92.77 118	97.74 106	98.12 49

EPP-MVSNet		92.13 67	93.06 57	91.05 96	93.66 103	97.30 71	92.18 122	87.90 105	90.24 85	83.63 103	86.14 71	90.52 68	90.76 98	94.82 75	94.38 74	98.18 70	97.98 53

ACMP	89.13 9	92.03 68	91.70 79	92.41 73	94.92 77	96.44 101	93.95 88	89.96 67	91.81 66	85.48 90	90.97 43	79.12 128	92.42 75	93.28 117	92.55 122	97.76 104	97.74 66
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
LS3D		91.97 69	90.98 90	93.12 60	97.03 52	97.09 83	95.33 60	95.59 22	92.47 58	79.26 126	81.60 108	82.77 100	94.39 46	94.28 88	94.23 76	97.14 136	94.45 160

casdiffmvs_mvg		91.94 70	91.25 85	92.75 68	93.41 106	97.19 78	95.48 56	89.77 70	89.86 97	86.41 71	81.02 112	82.23 108	92.93 69	95.44 61	95.61 54	98.51 26	97.40 77
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
PVSNet_Blended_VisFu		91.92 71	92.39 69	91.36 94	95.45 72	97.85 54	92.25 121	89.54 78	88.53 115	87.47 62	79.82 119	90.53 66	85.47 158	96.31 46	95.16 63	97.99 90	98.56 25

IS_MVSNet		91.87 72	93.35 54	90.14 109	94.09 87	97.73 61	93.09 109	88.12 101	88.71 112	79.98 123	84.49 81	90.63 65	87.49 137	97.07 22	96.96 17	98.07 81	97.88 62

LGP-MVS_train		91.83 73	92.04 74	91.58 86	95.46 70	96.18 105	95.97 50	89.85 68	90.45 81	77.76 129	91.92 39	80.07 124	92.34 77	94.27 89	93.47 96	98.11 76	97.90 61

MVS_Test		91.81 74	92.19 71	91.37 93	93.24 108	96.95 87	94.43 70	86.25 125	91.45 70	83.45 104	86.31 68	85.15 87	92.93 69	93.99 95	94.71 71	97.92 94	96.77 100

MVSTER		91.73 75	91.61 80	91.86 80	93.18 109	94.56 121	94.37 72	87.90 105	90.16 89	88.69 52	89.23 52	81.28 116	88.92 125	95.75 56	93.95 84	98.12 74	96.37 115

casdiffmvs		91.72 76	91.16 87	92.38 74	93.16 111	97.15 79	93.95 88	89.49 79	91.58 69	86.03 76	80.75 114	80.95 117	93.16 65	95.25 63	95.22 62	98.50 29	97.23 84
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
ACMM	88.76 10	91.70 77	90.43 94	93.19 57	95.56 67	95.14 118	93.35 106	91.48 53	92.26 60	87.12 65	84.02 84	79.34 127	93.99 53	94.07 94	92.68 119	97.62 118	95.50 143
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
viewmanbaseed2359cas		91.57 78	91.09 88	92.12 76	93.36 107	97.26 72	94.02 84	89.62 76	90.50 80	84.95 99	82.00 104	81.36 114	92.69 72	94.47 87	95.04 65	98.09 79	97.00 92

UGNet		91.52 79	93.41 53	89.32 115	94.13 85	97.15 79	91.83 131	89.01 85	90.62 76	85.86 81	86.83 63	91.73 56	77.40 198	94.68 79	94.43 73	97.71 108	98.40 38
Wanjuan Su, Qingshan Xu, Wenbing Tao: Uncertainty-guided Multi-view Stereo Network for Depth Estimation. IEEE Transactions on Circuits and Systems for Video Technology, 2022
diffmvs		91.37 80	91.09 88	91.70 84	92.71 122	96.47 98	94.03 83	88.78 88	92.74 56	85.43 92	83.63 88	80.37 120	91.76 85	93.39 113	93.78 87	97.50 122	97.23 84
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
DCV-MVSNet		91.24 81	91.26 84	91.22 95	92.84 118	93.44 149	93.82 93	86.75 121	91.33 71	85.61 86	84.00 85	85.46 86	91.27 88	92.91 119	93.62 90	97.02 144	98.05 52

diffmvs_AUTHOR		91.22 82	90.82 92	91.68 85	92.69 123	96.56 94	94.05 82	88.87 86	91.87 63	85.08 97	82.26 102	80.04 125	91.84 82	93.80 102	93.93 85	97.56 119	97.26 82

baseline		91.19 83	91.89 76	90.38 100	92.76 119	95.04 119	93.55 101	84.54 142	92.92 53	85.71 84	86.68 67	86.96 76	89.28 116	92.00 137	92.62 121	96.46 166	96.99 93

OPM-MVS		91.08 84	89.34 106	93.11 61	96.18 61	96.13 106	96.39 43	92.39 43	82.97 163	81.74 109	82.55 100	80.20 123	93.97 55	94.62 80	93.23 103	98.00 89	95.73 138
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
DI_MVS_pp		91.05 85	90.15 98	92.11 77	92.67 124	96.61 92	96.03 48	88.44 95	90.25 84	85.92 79	73.73 152	84.89 89	91.92 80	94.17 92	94.07 82	97.68 113	97.31 81

thisisatest0530		91.04 86	91.74 77	90.21 104	92.93 117	97.00 85	92.06 127	87.63 116	90.74 72	81.51 110	86.81 64	82.48 102	89.23 117	94.81 76	93.03 114	97.90 95	97.33 80

tttt0517		91.01 87	91.71 78	90.19 106	92.98 113	97.07 84	91.96 130	87.63 116	90.61 77	81.42 111	86.76 65	82.26 107	89.23 117	94.86 74	93.03 114	97.90 95	97.36 78

test2506		90.93 88	89.20 109	92.95 64	94.97 75	98.30 39	94.53 68	90.25 64	89.91 95	88.39 54	83.23 90	64.17 200	90.69 99	96.75 32	96.10 46	98.87 8	95.97 131

UA-Net		90.81 89	92.58 64	88.74 121	94.87 79	97.44 69	92.61 115	88.22 99	82.35 167	78.93 127	85.20 78	95.61 39	79.56 193	96.52 38	96.57 25	98.23 64	94.37 161

baseline1		90.81 89	90.29 95	91.42 90	93.67 102	95.86 111	93.94 90	89.69 74	89.29 105	82.85 107	82.91 93	80.30 121	89.60 109	95.05 66	94.79 70	98.80 12	93.82 169

viewmacassd2359aftdt		90.80 91	89.95 102	91.78 81	93.17 110	97.14 82	93.99 85	89.56 77	87.66 120	83.65 102	78.82 123	80.23 122	92.23 78	93.74 104	95.11 64	98.10 77	96.97 94

FA-MVS(training)		90.79 92	91.33 83	90.17 107	93.76 100	97.22 76	92.74 113	77.79 197	90.60 78	88.03 55	78.80 124	87.41 74	91.00 93	95.40 62	93.43 98	97.70 110	96.46 111

ECVR-MVS		90.77 93	89.27 107	92.52 69	94.97 75	98.30 39	94.53 68	90.25 64	89.91 95	85.80 82	73.64 153	74.31 154	90.69 99	96.75 32	96.10 46	98.87 8	95.91 135

CHOSEN 280x420		90.77 93	92.14 72	89.17 117	93.86 97	92.81 171	93.16 107	80.22 187	90.21 86	84.67 100	89.89 50	91.38 60	90.57 103	94.94 69	92.11 130	92.52 207	93.65 171

CANet_DTU		90.74 95	92.93 61	88.19 127	94.36 81	96.61 92	94.34 74	84.66 139	90.66 74	68.75 177	90.41 47	86.89 77	89.78 108	95.46 60	94.87 68	97.25 129	95.62 140

viewmambaseed2359dif		90.70 96	89.81 104	91.73 83	92.66 125	96.10 107	93.97 86	88.69 91	89.92 94	86.12 74	80.79 113	80.73 119	91.92 80	91.13 152	92.81 117	97.06 141	97.20 87

FC-MVSNet-train		90.55 97	90.19 97	90.97 97	93.78 99	95.16 117	92.11 126	88.85 87	87.64 121	83.38 105	84.36 83	78.41 134	89.53 110	94.69 78	93.15 109	98.15 71	97.92 58

Vis-MVSNet (Re-imp)		90.54 98	92.76 62	87.94 131	93.73 101	96.94 88	92.17 124	87.91 104	88.77 111	76.12 137	83.68 87	90.80 61	79.49 194	96.34 45	96.35 32	98.21 66	96.46 111

test1111		90.47 99	89.10 111	92.07 78	94.92 77	98.30 39	94.17 81	90.30 63	89.56 102	83.92 101	73.25 160	73.66 155	90.26 105	96.77 30	96.14 44	98.87 8	96.04 128

MSDG		90.42 100	88.25 122	92.94 65	96.67 56	94.41 127	93.96 87	92.91 41	89.59 101	86.26 72	76.74 136	80.92 118	90.43 104	92.60 125	92.08 132	97.44 125	91.41 186

PatchMatch-RL		90.30 101	88.93 113	91.89 79	95.41 73	95.68 112	90.94 136	88.67 92	89.80 98	86.95 67	85.90 72	72.51 157	92.46 74	93.56 108	92.18 128	96.93 153	92.89 179

GBi-Net		90.21 102	90.11 99	90.32 102	88.66 168	93.65 145	94.25 77	85.78 129	90.03 90	85.56 87	77.38 129	86.13 80	89.38 113	93.97 96	94.16 78	98.31 51	95.47 144

test1		90.21 102	90.11 99	90.32 102	88.66 168	93.65 145	94.25 77	85.78 129	90.03 90	85.56 87	77.38 129	86.13 80	89.38 113	93.97 96	94.16 78	98.31 51	95.47 144

FMVSNet3		90.19 104	90.06 101	90.34 101	88.69 167	93.85 137	94.58 67	85.78 129	90.03 90	85.56 87	77.38 129	86.13 80	89.22 119	93.29 116	94.36 75	98.20 67	95.40 148

ET-MVSNet_ETH3D		89.93 105	90.84 91	88.87 119	79.60 221	96.19 104	94.43 70	86.56 122	90.63 75	80.75 119	90.71 45	77.78 141	93.73 59	91.36 146	93.45 97	98.15 71	95.77 137

PMMVS		89.88 106	91.19 86	88.35 125	89.73 158	91.97 191	90.62 139	81.92 174	90.57 79	80.58 121	92.16 36	86.85 78	91.17 90	92.31 130	91.35 146	96.11 172	93.11 178

Anonymous20231211		89.82 107	88.18 123	91.74 82	92.52 126	96.09 108	93.38 105	89.30 83	88.95 108	85.90 80	64.55 202	84.39 90	92.41 76	92.24 133	93.06 112	96.93 153	97.95 55

Effi-MVS+		89.79 108	89.83 103	89.74 111	92.98 113	96.45 100	93.48 103	84.24 144	87.62 122	76.45 135	81.76 106	77.56 144	93.48 62	94.61 81	93.59 91	97.82 99	97.22 86

viewdifsd2359ckpt11		89.68 109	88.67 116	90.86 98	92.35 127	95.23 114	91.72 133	88.40 97	88.84 109	86.14 73	80.75 114	78.17 137	90.95 94	90.02 172	91.15 149	95.59 184	96.50 109

RPSCF		89.68 109	89.24 108	90.20 105	92.97 115	92.93 167	92.30 119	87.69 113	90.44 82	85.12 95	91.68 40	85.84 85	90.69 99	87.34 196	86.07 198	92.46 208	90.37 196

viewmsd2359difaftdt		89.67 111	88.66 117	90.85 99	92.35 127	95.23 114	91.72 133	88.40 97	88.80 110	86.12 74	80.75 114	78.20 136	90.94 96	90.02 172	91.15 149	95.59 184	96.50 109

FMVSNet2		89.61 112	89.14 110	90.16 108	88.66 168	93.65 145	94.25 77	85.44 133	88.57 114	84.96 98	73.53 155	83.82 92	89.38 113	94.23 90	94.68 72	98.31 51	95.47 144

tfpn200view9		89.55 113	87.86 128	91.53 88	93.90 95	97.26 72	94.31 76	89.74 71	85.87 136	81.15 114	76.46 138	70.38 166	91.76 85	94.92 70	93.51 92	98.28 57	96.61 104

thres200		89.49 114	87.72 130	91.55 87	93.95 92	97.25 74	94.34 74	89.74 71	85.66 139	81.18 113	76.12 142	70.19 169	91.80 83	94.92 70	93.51 92	98.27 58	96.40 114

thres400		89.40 115	87.58 135	91.53 88	94.06 89	97.21 77	94.19 80	89.83 69	85.69 138	81.08 116	75.50 145	69.76 170	91.80 83	94.79 77	93.51 92	98.20 67	96.60 105

thres100view900		89.36 116	87.61 133	91.39 91	93.90 95	96.86 90	94.35 73	89.66 75	85.87 136	81.15 114	76.46 138	70.38 166	91.17 90	94.09 93	93.43 98	98.13 73	96.16 123

Vis-MVSNet		89.36 116	91.49 82	86.88 142	92.10 131	97.60 66	92.16 125	85.89 127	84.21 152	75.20 139	82.58 98	87.13 75	77.40 198	95.90 54	95.63 53	98.51 26	97.36 78
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
GeoE		89.29 118	88.68 115	89.99 110	92.75 121	96.03 109	93.07 111	83.79 151	86.98 126	81.34 112	74.72 148	78.92 129	91.22 89	93.31 115	93.21 106	97.78 102	97.60 71

thres600view7		89.28 119	87.47 138	91.39 91	94.12 86	97.25 74	93.94 90	89.74 71	85.62 141	80.63 120	75.24 147	69.33 172	91.66 87	94.92 70	93.23 103	98.27 58	96.72 101

baseline2		88.97 120	89.50 105	88.36 124	91.14 143	95.30 113	90.13 150	85.17 136	87.24 123	80.80 118	84.46 82	78.44 133	85.60 155	93.54 109	91.87 136	97.31 127	95.66 139

IterMVS-LS		88.60 121	88.45 118	88.78 120	92.02 132	92.44 181	92.00 129	83.57 155	86.52 132	78.90 128	78.61 126	81.34 115	89.12 120	90.68 160	93.18 107	97.10 138	96.35 116
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
CHOSEN 1792x2688		88.57 122	87.82 129	89.44 114	95.46 70	96.89 89	93.74 96	85.87 128	89.63 100	77.42 132	61.38 208	83.31 95	88.80 127	93.44 112	93.16 108	95.37 191	96.95 96

Fast-Effi-MVS+		88.56 123	87.99 126	89.22 116	91.56 138	95.21 116	92.29 120	82.69 162	86.82 127	77.73 130	76.24 141	73.39 156	93.36 64	94.22 91	93.64 89	97.65 115	96.43 113

CDS-MVSNet		88.34 124	88.71 114	87.90 132	90.70 151	94.54 122	92.38 117	86.02 126	80.37 176	79.42 125	79.30 120	83.43 94	82.04 181	93.39 113	94.01 83	96.86 159	95.93 134
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
EPNet_dtu		88.32 125	90.61 93	85.64 154	96.79 55	92.27 183	92.03 128	90.31 62	89.05 107	65.44 198	89.43 51	85.90 84	74.22 207	92.76 120	92.09 131	95.02 196	92.76 180
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
IB-MVS	85.10 14	87.98 126	87.97 127	87.99 130	94.55 80	96.86 90	84.52 202	88.21 100	86.48 134	88.54 53	74.41 151	77.74 142	74.10 209	89.65 179	92.85 116	98.06 83	97.80 65
Christian Sormann, Mattia Rossi, Andreas Kuhn and Friedrich Fraundorfer: IB-MVS: An Iterative Algorithm for Deep Multi-View Stereo based on Binary Decisions. BMVC 2021
HyFIR lowres test		87.87 127	86.42 144	89.57 112	95.56 67	96.99 86	92.37 118	84.15 146	86.64 129	77.17 133	57.65 214	83.97 91	91.08 92	92.09 136	92.44 123	97.09 139	95.16 151

MS-PatchMatch		87.63 128	87.61 133	87.65 135	93.95 92	94.09 133	92.60 116	81.52 179	86.64 129	76.41 136	73.46 157	85.94 83	85.01 162	92.23 134	90.00 177	96.43 168	90.93 192

COLMAP_ROB	84.39 15	87.61 129	86.03 149	89.46 113	95.54 69	94.48 124	91.77 132	90.14 66	87.16 124	75.50 138	73.41 158	76.86 148	87.33 139	90.05 171	89.76 183	96.48 165	90.46 195
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
Effi-MVS+-dtu		87.51 130	88.13 124	86.77 144	91.10 144	94.90 120	90.91 137	82.67 163	83.47 159	71.55 156	81.11 111	77.04 146	89.41 112	92.65 124	91.68 142	95.00 197	96.09 126

FMVSNet1		87.33 131	86.00 151	88.89 118	87.13 194	92.83 170	93.08 110	84.46 143	81.35 172	82.20 108	66.33 188	77.96 139	88.96 122	93.97 96	94.16 78	97.54 121	95.38 149

dmvs_re		87.31 132	86.10 147	88.74 121	89.84 155	94.28 130	92.66 114	89.41 80	82.61 165	74.69 140	74.69 149	69.47 171	87.78 132	92.38 129	93.23 103	98.03 85	96.02 130

ACMH	85.51 13	87.31 132	86.59 142	88.14 128	93.96 91	94.51 123	89.00 172	87.99 102	81.58 170	70.15 167	78.41 127	71.78 162	90.60 102	91.30 147	91.99 134	97.17 133	96.58 106
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ACMH+	85.75 12	87.19 134	86.02 150	88.56 123	93.42 105	94.41 127	89.91 156	87.66 115	83.45 160	72.25 154	76.42 140	71.99 161	90.78 97	89.86 174	90.94 151	97.32 126	95.11 153

test-LLR		86.88 135	88.28 120	85.24 158	91.22 141	92.07 187	87.41 185	83.62 153	84.58 145	69.33 173	83.00 91	82.79 98	84.24 166	92.26 131	89.81 180	95.64 182	93.44 172

UniMVSNet_NR-MVSNet		86.80 136	85.86 154	87.89 133	88.17 174	94.07 134	90.15 148	88.51 94	84.20 153	73.45 147	72.38 164	70.30 168	88.95 123	90.25 165	92.21 127	98.12 74	97.62 69

CostFormer		86.78 137	86.05 148	87.62 137	92.15 130	93.20 158	91.55 135	75.83 202	88.11 118	85.29 93	81.76 106	76.22 150	87.80 131	84.45 208	85.21 204	93.12 202	93.42 174

USDC		86.73 138	85.96 152	87.63 136	91.64 135	93.97 135	92.76 112	84.58 141	88.19 116	70.67 164	80.10 118	67.86 179	89.43 111	91.81 139	89.77 182	96.69 163	90.05 199

MDTV_nov1_ep13		86.64 139	87.50 137	85.65 153	90.73 149	93.69 143	89.96 154	78.03 196	89.48 104	76.85 134	84.92 79	82.42 104	86.14 152	86.85 200	86.15 197	92.17 209	88.97 204

Fast-Effi-MVS+-dtu		86.25 140	87.70 131	84.56 167	90.37 154	93.70 142	90.54 140	78.14 194	83.50 158	65.37 199	81.59 109	75.83 152	86.09 154	91.70 141	91.70 140	96.88 157	95.84 136

SCA		86.25 140	87.52 136	84.77 163	91.59 136	93.90 136	89.11 169	73.25 214	90.38 83	72.84 150	83.26 89	83.79 93	88.49 129	86.07 203	85.56 201	93.33 200	89.67 201

UniMVSNet (Re)		86.22 142	85.46 159	87.11 139	88.34 172	94.42 126	89.65 162	87.10 120	84.39 149	74.61 141	70.41 172	68.10 177	85.10 161	91.17 150	91.79 138	97.84 98	97.94 56

FC-MVSNet-test		86.15 143	89.10 111	82.71 192	89.83 156	93.18 159	87.88 182	84.69 138	86.54 131	62.18 208	82.39 101	83.31 95	74.18 208	92.52 127	91.86 137	97.50 122	93.88 168

DU-MVS		86.12 144	84.81 162	87.66 134	87.77 181	93.78 139	90.15 148	87.87 107	84.40 147	73.45 147	70.59 169	64.82 197	88.95 123	90.14 166	92.33 124	97.76 104	97.62 69

TESTMET0.1,1		86.11 145	88.28 120	83.59 179	87.80 179	92.07 187	87.41 185	77.12 199	84.58 145	69.33 173	83.00 91	82.79 98	84.24 166	92.26 131	89.81 180	95.64 182	93.44 172

test-mter		86.09 146	88.38 119	83.43 182	87.89 178	92.61 175	86.89 190	77.11 200	84.30 150	68.62 179	82.57 99	82.45 103	84.34 165	92.40 128	90.11 174	95.74 177	94.21 164

pmmvs4		86.00 147	84.28 166	88.00 129	87.80 179	92.01 190	89.94 155	84.91 137	86.79 128	80.98 117	73.41 158	66.34 188	88.12 130	89.31 182	88.90 191	96.24 171	93.20 177

EPMVS		85.77 148	86.24 146	85.23 159	92.76 119	93.78 139	89.91 156	73.60 210	90.19 87	74.22 142	82.18 103	78.06 138	87.55 136	85.61 205	85.38 203	93.32 201	88.48 208

thisisatest0515		85.70 149	87.00 139	84.19 172	88.16 175	93.67 144	84.20 204	84.14 147	83.39 161	72.91 149	76.79 135	74.75 153	78.82 196	92.57 126	91.26 147	96.94 150	96.56 108

PatchmatchNet		85.70 149	86.65 141	84.60 166	91.79 133	93.40 150	89.27 165	73.62 209	90.19 87	72.63 152	82.74 97	81.93 112	87.64 134	84.99 206	84.29 208	92.64 206	89.00 203
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
test0.0.03 1		85.58 151	87.69 132	83.11 185	91.22 141	92.54 178	85.60 201	83.62 153	85.66 139	67.84 184	82.79 96	79.70 126	73.51 211	91.15 151	90.79 153	96.88 157	91.23 189

TranMVSNet+NR-MVSNet		85.57 152	84.41 165	86.92 141	87.67 184	93.34 152	90.31 144	88.43 96	83.07 162	70.11 168	69.99 175	65.28 192	86.96 142	89.73 176	92.27 125	98.06 83	97.17 89

CR-MVSNet		85.48 153	86.29 145	84.53 168	91.08 146	92.10 185	89.18 167	73.30 212	84.75 143	71.08 161	73.12 162	77.91 140	86.27 150	91.48 143	90.75 156	96.27 170	93.94 166

NR-MVSNet		85.46 154	84.54 164	86.52 147	88.33 173	93.78 139	90.45 141	87.87 107	84.40 147	71.61 155	70.59 169	62.09 207	82.79 177	91.75 140	91.75 139	98.10 77	97.44 75

IterMVS-SCA-FT		85.44 155	86.71 140	83.97 176	90.59 152	90.84 204	89.73 160	78.34 193	84.07 156	66.40 193	77.27 134	78.66 131	83.06 174	91.20 148	90.10 175	95.72 179	94.78 155

Baseline_NR-MVSNet		85.28 156	83.42 174	87.46 138	87.77 181	90.80 206	89.90 158	87.69 113	83.93 157	74.16 143	64.72 200	66.43 187	87.48 138	90.14 166	90.83 152	97.73 107	97.11 90

IterMVS		85.25 157	86.49 143	83.80 177	90.42 153	90.77 207	90.02 152	78.04 195	84.10 154	66.27 194	77.28 133	78.41 134	83.01 175	90.88 154	89.72 184	95.04 195	94.24 162
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
GA-MVS		85.08 158	85.65 156	84.42 169	89.77 157	94.25 131	89.26 166	84.62 140	81.19 173	62.25 207	75.72 144	68.44 176	84.14 169	93.57 107	91.68 142	96.49 164	94.71 157

dps		85.00 159	83.21 179	87.08 140	90.73 149	92.55 177	89.34 164	75.29 204	84.94 142	87.01 66	79.27 121	67.69 180	87.27 140	84.22 209	83.56 209	92.83 205	90.25 197

TDRefinement		84.97 160	83.39 175	86.81 143	92.97 115	94.12 132	92.18 122	87.77 111	82.78 164	71.31 159	68.43 178	68.07 178	81.10 189	89.70 178	89.03 190	95.55 188	91.62 184

TAMVS		84.94 161	84.95 160	84.93 162	88.82 164	93.18 159	88.44 178	81.28 181	77.16 195	73.76 146	75.43 146	76.57 149	82.04 181	90.59 161	90.79 153	95.22 193	90.94 191

RPMNet		84.82 162	85.90 153	83.56 180	91.10 144	92.10 185	88.73 176	71.11 217	84.75 143	68.79 176	73.56 154	77.62 143	85.33 159	90.08 170	89.43 186	96.32 169	93.77 170

UniMVSNet_ETH3D		84.57 163	81.40 197	88.28 126	89.34 162	94.38 129	90.33 142	86.50 123	74.74 208	77.52 131	59.90 212	62.04 208	88.78 128	88.82 189	92.65 120	97.22 130	97.24 83

pm-mvs1		84.55 164	83.46 171	85.82 150	88.16 175	93.39 151	89.05 171	85.36 135	74.03 209	72.43 153	65.08 196	71.11 163	82.30 180	93.48 110	91.70 140	97.64 116	95.43 147

anonymousdsp		84.51 165	85.85 155	82.95 189	86.30 205	93.51 148	85.77 199	80.38 186	78.25 190	63.42 205	73.51 156	72.20 159	84.64 164	93.21 118	92.16 129	97.19 132	98.14 47

v2v482		84.51 165	83.05 181	86.20 149	87.25 190	93.28 155	90.22 146	85.40 134	79.94 182	69.78 170	67.74 180	65.15 194	87.57 135	89.12 185	90.55 162	96.97 146	95.60 141

V42		84.48 167	83.36 177	85.79 152	87.14 193	93.28 155	90.03 151	83.98 149	80.30 177	71.20 160	66.90 185	67.17 181	85.55 156	89.35 180	90.27 167	96.82 160	96.27 121

FMVSNet5		84.47 168	84.72 163	84.18 173	83.30 215	88.43 212	88.09 180	79.42 190	84.25 151	74.14 144	73.15 161	78.74 130	83.65 172	91.19 149	91.19 148	96.46 166	86.07 213

v8		84.45 169	83.30 178	85.80 151	87.53 186	92.95 165	90.31 144	82.46 167	80.46 175	71.43 157	66.99 183	67.16 182	86.14 152	89.26 183	90.22 169	96.94 150	96.06 127

v10		84.18 170	83.17 180	85.37 155	87.34 188	92.68 173	90.32 143	81.33 180	79.93 183	69.23 175	66.33 188	65.74 190	87.03 141	90.84 155	90.38 164	96.97 146	96.29 120

tpm cat1		84.13 171	81.99 191	86.63 146	91.74 134	91.50 198	90.68 138	75.69 203	86.12 135	85.44 91	72.39 163	70.72 164	85.16 160	80.89 217	81.56 213	91.07 215	90.71 193

ADS-MVSNet		84.08 172	84.95 160	83.05 188	91.53 140	91.75 194	88.16 179	70.70 218	89.96 93	69.51 172	78.83 122	76.97 147	86.29 149	84.08 210	84.60 206	92.13 211	88.48 208

TinyColmap		84.04 173	82.01 190	86.42 148	90.87 147	91.84 192	88.89 174	84.07 148	82.11 169	69.89 169	71.08 167	60.81 213	89.04 121	90.52 162	89.19 188	95.76 176	88.50 207

v1144		84.03 174	82.88 182	85.37 155	87.17 192	93.15 162	90.18 147	83.31 158	78.83 186	67.85 183	65.99 190	64.99 195	86.79 144	90.75 157	90.33 166	96.90 155	96.15 124

PatchT		83.86 175	85.51 158	81.94 198	88.41 171	91.56 197	78.79 216	71.57 216	84.08 155	71.08 161	70.62 168	76.13 151	86.27 150	91.48 143	90.75 156	95.52 189	93.94 166

CVMVSNet		83.83 176	85.53 157	81.85 199	89.60 159	90.92 202	87.81 183	83.21 159	80.11 179	60.16 212	76.47 137	78.57 132	76.79 200	89.76 175	90.13 170	93.51 199	92.75 181

tfpnnormal		83.80 177	81.26 199	86.77 144	89.60 159	93.26 157	89.72 161	87.60 118	72.78 210	70.44 165	60.53 211	61.15 212	85.55 156	92.72 121	91.44 144	97.71 108	96.92 97

tpmrst		83.72 178	83.45 172	84.03 175	92.21 129	91.66 195	88.74 175	73.58 211	88.14 117	72.67 151	77.37 132	72.11 160	86.34 148	82.94 213	82.05 212	90.63 217	89.86 200

v148		83.61 179	82.10 188	85.37 155	87.34 188	92.94 166	87.48 184	85.72 132	78.92 185	73.87 145	65.71 193	64.69 198	81.78 185	87.82 192	89.35 187	96.01 173	95.26 150

v1192		83.56 180	82.35 185	84.98 160	86.84 199	92.84 168	90.01 153	82.70 161	78.54 187	66.48 191	64.88 198	62.91 202	86.91 143	90.72 158	90.25 168	96.94 150	96.32 118

v144192		83.48 181	82.23 186	84.94 161	86.65 200	92.84 168	89.63 163	82.48 166	77.87 191	67.36 187	65.33 195	63.50 201	86.51 146	89.72 177	89.99 178	97.03 143	96.35 116

pmmvs5		83.37 182	82.68 183	84.18 173	87.13 194	93.18 159	86.74 191	82.08 172	76.48 199	67.28 188	71.26 166	62.70 204	84.71 163	90.77 156	90.12 173	97.15 134	94.24 162

v1921920		83.30 183	82.09 189	84.70 164	86.59 203	92.67 174	89.82 159	82.23 170	78.32 188	65.76 196	64.64 201	62.35 205	86.78 145	90.34 164	90.02 176	97.02 144	96.31 119

tpm		83.16 184	83.64 169	82.60 194	90.75 148	91.05 201	88.49 177	73.99 207	82.36 166	67.08 190	78.10 128	68.79 173	84.17 168	85.95 204	85.96 199	91.09 214	93.23 176

WR-MVS		83.14 185	83.38 176	82.87 190	87.55 185	93.29 154	86.36 195	84.21 145	80.05 180	66.41 192	66.91 184	66.92 184	75.66 205	88.96 187	90.56 161	97.05 142	96.96 95

SixPastTwentyTwo		83.12 186	83.44 173	82.74 191	87.71 183	93.11 163	82.30 209	82.33 168	79.24 184	64.33 202	78.77 125	62.75 203	84.11 170	88.11 191	87.89 193	95.70 180	94.21 164

CP-MVSNet		83.11 187	82.15 187	84.23 171	87.20 191	92.70 172	86.42 194	83.53 156	77.83 192	67.67 185	66.89 186	60.53 215	82.47 178	89.23 184	90.65 160	98.08 80	97.20 87

MIMVSNet		82.97 188	84.00 168	81.77 200	82.23 217	92.25 184	87.40 187	72.73 215	81.48 171	69.55 171	68.79 177	72.42 158	81.82 184	92.23 134	92.25 126	96.89 156	88.61 206

v1240		82.88 189	81.66 193	84.29 170	86.46 204	92.52 180	89.06 170	81.82 176	77.16 195	65.09 200	64.17 203	61.50 210	86.36 147	90.12 168	90.13 170	96.95 149	96.04 128

WR-MVS_H		82.86 190	82.66 184	83.10 186	87.44 187	93.33 153	85.71 200	83.20 160	77.36 194	68.20 182	66.37 187	65.23 193	76.05 204	89.35 180	90.13 170	97.99 90	96.89 98

TransMVSNet (Re)		82.67 191	80.93 202	84.69 165	88.71 166	91.50 198	87.90 181	87.15 119	71.54 215	68.24 181	63.69 204	64.67 199	78.51 197	91.65 142	90.73 158	97.64 116	92.73 182

PS-CasMVS		82.53 192	81.54 195	83.68 178	87.08 196	92.54 178	86.20 196	83.46 157	76.46 200	65.73 197	65.71 193	59.41 220	81.61 186	89.06 186	90.55 162	98.03 85	97.07 91

PEN-MVS		82.49 193	81.58 194	83.56 180	86.93 197	92.05 189	86.71 192	83.84 150	76.94 197	64.68 201	67.24 181	60.11 216	81.17 188	87.78 193	90.70 159	98.02 87	96.21 122

LTVRE_ROB	81.71 16	82.44 194	81.84 192	83.13 184	89.01 163	92.99 164	88.90 173	82.32 169	66.26 221	54.02 222	74.68 150	59.62 219	88.87 126	90.71 159	92.02 133	95.68 181	96.62 103
Andreas Kuhn, Heiko HirschmÃ¼ller, Daniel Scharstein, Helmut Mayer: A TV Prior for High-Quality Scalable Multi-View Stereo Reconstruction. International Journal of Computer Vision 2016
v7n		82.25 195	81.54 195	83.07 187	85.55 209	92.58 176	86.68 193	81.10 184	76.54 198	65.97 195	62.91 205	60.56 214	82.36 179	91.07 153	90.35 165	96.77 162	96.80 99

testgi		81.94 196	84.09 167	79.43 205	89.53 161	90.83 205	82.49 208	81.75 177	80.59 174	59.46 214	82.82 95	65.75 189	67.97 213	90.10 169	89.52 185	95.39 190	89.03 202

gg-mvs-nofinetune		81.83 197	83.58 170	79.80 204	91.57 137	96.54 96	93.79 94	68.80 221	62.71 225	43.01 230	55.28 217	85.06 88	83.65 172	96.13 49	94.86 69	97.98 93	94.46 159

DTE-MVSNet		81.76 198	81.04 200	82.60 194	86.63 201	91.48 200	85.97 198	83.70 152	76.45 201	62.44 206	67.16 182	59.98 217	78.98 195	87.15 197	89.93 179	97.88 97	95.12 152

EG-PatchMatch MVS		81.70 199	81.31 198	82.15 197	88.75 165	93.81 138	87.14 188	78.89 192	71.57 213	64.12 204	61.20 210	68.46 175	76.73 202	91.48 143	90.77 155	97.28 128	91.90 183

pmmvs6		80.90 200	78.77 206	83.38 183	85.84 206	91.61 196	86.01 197	82.54 165	64.17 222	70.43 166	54.14 221	67.06 183	80.73 190	90.50 163	89.17 189	94.74 198	94.75 156

MDTV_nov1_ep13_2view		80.43 201	80.94 201	79.84 203	84.82 212	90.87 203	84.23 203	73.80 208	80.28 178	64.33 202	70.05 174	68.77 174	79.67 191	84.83 207	83.50 210	92.17 209	88.25 210

PM-MVS		80.29 202	79.30 205	81.45 201	81.91 218	88.23 213	82.61 207	79.01 191	79.99 181	67.15 189	69.07 176	51.39 225	82.92 176	87.55 195	85.59 200	95.08 194	93.28 175

pmnet_mix02		80.14 203	80.21 204	80.06 202	86.61 202	89.66 209	80.40 213	82.20 171	82.29 168	61.35 209	71.52 165	66.67 186	76.75 201	82.55 214	80.18 217	93.05 203	88.62 205

CMPMVS	61.19 17	79.86 204	77.46 212	82.66 193	91.54 139	91.82 193	83.25 205	81.57 178	70.51 217	68.64 178	59.89 213	66.77 185	79.63 192	84.00 211	84.30 207	91.34 213	84.89 216
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
pmmvs-eth3d		79.78 205	77.58 210	82.34 196	81.57 219	87.46 216	82.92 206	81.28 181	75.33 207	71.34 158	61.88 206	52.41 224	81.59 187	87.56 194	86.90 196	95.36 192	91.48 185

EU-MVSNet		78.43 206	80.25 203	76.30 210	83.81 214	87.27 218	80.99 211	79.52 189	76.01 202	54.12 221	70.44 171	64.87 196	67.40 215	86.23 202	85.54 202	91.95 212	91.41 186

MVS-HIRNet		78.16 207	77.57 211	78.83 206	85.83 207	87.76 214	76.67 217	70.22 219	75.82 205	67.39 186	55.61 216	70.52 165	81.96 183	86.67 201	85.06 205	90.93 216	81.58 219

Anonymous20231206		78.09 208	78.11 209	78.07 208	85.19 211	89.17 210	80.99 211	81.24 183	75.46 206	58.25 216	54.78 220	59.90 218	66.73 216	88.94 188	88.26 192	96.01 173	90.25 197

gm-plane-assit		77.65 209	78.50 207	76.66 209	87.96 177	85.43 220	64.70 226	74.50 205	64.15 223	51.26 225	61.32 209	58.17 221	84.11 170	95.16 65	93.83 86	97.45 124	91.41 186

N_pmnet		77.55 210	76.68 213	78.56 207	85.43 210	87.30 217	78.84 215	81.88 175	78.30 189	60.61 210	61.46 207	62.15 206	74.03 210	82.04 215	80.69 216	90.59 218	84.81 217

test20.03		76.41 211	78.49 208	73.98 212	85.64 208	87.50 215	75.89 218	80.71 185	70.84 216	51.07 226	68.06 179	61.40 211	54.99 222	88.28 190	87.20 195	95.58 187	86.15 212

MDA-MVSNet-bldmvs		73.81 212	72.56 216	75.28 211	72.52 226	88.87 211	74.95 220	82.67 163	71.57 213	55.02 219	65.96 191	42.84 232	76.11 203	70.61 224	81.47 214	90.38 219	86.59 211

MIMVSNet1		73.19 213	73.70 214	72.60 215	65.42 229	86.69 219	75.56 219	79.65 188	67.87 220	55.30 218	45.24 225	56.41 222	63.79 218	86.98 198	87.66 194	95.85 175	85.04 215

new-patchmatchnet		72.32 214	71.09 217	73.74 213	81.17 220	84.86 221	72.21 223	77.48 198	68.32 219	54.89 220	55.10 218	49.31 228	63.68 219	79.30 219	76.46 220	93.03 204	84.32 218

new_pmnet		72.29 215	73.25 215	71.16 217	75.35 223	81.38 222	73.72 222	69.27 220	75.97 203	49.84 227	56.27 215	56.12 223	69.08 212	81.73 216	80.86 215	89.72 221	80.44 221

pmmvs3		71.13 216	71.06 218	71.21 216	73.54 225	80.19 223	71.69 224	64.86 223	62.04 226	52.10 223	54.92 219	48.00 230	75.03 206	83.75 212	83.24 211	90.04 220	85.27 214

FPMVS		69.87 217	67.10 220	73.10 214	84.09 213	78.35 225	79.40 214	76.41 201	71.92 211	57.71 217	54.06 222	50.04 226	56.72 220	71.19 223	68.70 223	84.25 223	75.43 223

GG-mvs-BLEND		62.84 218	90.21 96	30.91 227	0.57 236	94.45 125	86.99 189	0.34 233	88.71 112	0.98 236	81.55 110	91.58 58	0.86 233	92.66 123	91.43 145	95.73 178	91.11 190

PMVS	56.77 18	61.27 219	58.64 223	64.35 218	75.66 222	54.60 230	53.62 229	74.23 206	53.69 227	58.37 215	44.27 226	49.38 227	44.16 226	69.51 225	65.35 225	80.07 225	73.66 224
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
WB-MVS		60.76 220	66.86 221	53.64 220	82.24 216	72.70 226	48.70 232	82.04 173	63.91 224	12.91 235	64.77 199	49.00 229	22.74 230	75.95 221	75.36 221	73.22 229	66.33 227

Gipuma		58.52 221	56.17 224	61.27 219	67.14 228	58.06 229	52.16 230	68.40 222	69.00 218	45.02 229	22.79 228	20.57 235	55.11 221	76.27 220	79.33 219	79.80 226	67.16 226
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
test_method		58.10 222	64.61 222	50.51 222	28.26 234	41.71 233	61.28 227	32.07 229	75.92 204	52.04 224	47.94 223	61.83 209	51.80 223	79.83 218	63.95 227	77.60 227	81.05 220

PMMVS2		53.68 223	55.72 225	51.30 221	58.84 230	67.02 228	54.23 228	60.97 226	47.50 228	19.42 232	34.81 227	31.97 233	30.88 228	65.84 226	69.99 222	83.47 224	72.92 225

E-PMN		40.00 224	35.74 227	44.98 224	57.69 232	39.15 235	28.05 233	62.70 224	35.52 230	17.78 233	20.90 229	14.36 237	44.47 225	35.89 229	47.86 228	59.15 231	56.47 229

MVE	39.81 19	39.52 225	41.58 226	37.11 226	33.93 233	49.06 231	26.45 235	54.22 227	29.46 231	24.15 231	20.77 230	10.60 238	34.42 227	51.12 228	65.27 226	49.49 233	64.81 228
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
EMVS		39.04 226	34.32 228	44.54 225	58.25 231	39.35 234	27.61 234	62.55 225	35.99 229	16.40 234	20.04 231	14.77 236	44.80 224	33.12 230	44.10 229	57.61 232	52.89 230

testmvs		4.35 227	6.54 229	1.79 228	0.60 235	1.82 236	3.06 237	0.95 231	7.22 232	0.88 237	12.38 232	1.25 239	3.87 232	6.09 231	5.58 230	1.40 234	11.42 232

test123		3.48 228	5.31 230	1.34 229	0.20 237	1.52 237	2.17 238	0.58 232	6.13 233	0.31 238	9.85 233	0.31 240	3.90 231	2.65 232	5.28 231	0.87 235	11.46 231

uanet_test		0.00 229	0.00 231	0.00 230	0.00 238	0.00 238	0.00 239	0.00 234	0.00 234	0.00 239	0.00 234	0.00 241	0.00 234	0.00 233	0.00 232	0.00 236	0.00 233

sosnet-low-res		0.00 229	0.00 231	0.00 230	0.00 238	0.00 238	0.00 239	0.00 234	0.00 234	0.00 239	0.00 234	0.00 241	0.00 234	0.00 233	0.00 232	0.00 236	0.00 233

sosnet		0.00 229	0.00 231	0.00 230	0.00 238	0.00 238	0.00 239	0.00 234	0.00 234	0.00 239	0.00 234	0.00 241	0.00 234	0.00 233	0.00 232	0.00 236	0.00 233

TPM-MVS					98.33 29	97.85 54	97.06 36			89.97 41	93.26 32	97.16 25	93.12 67			97.79 100	95.95 132
Ray Leroy Khuboni and Hongjun Xu: Textureless Resilient Propagation Matching in Multiple View Stereosis (TPM-MVS). SATNAC 2025
RE-MVS-def										60.19 211

9.14												97.28 23

SR-MVS					98.93 18			96.00 16				97.75 15

Anonymous202405211			88.00 125		93.16 111	96.38 102	93.58 100	89.34 81	87.92 119		65.04 197	83.03 97	92.07 79	92.67 122	93.33 100	96.96 148	97.63 67

our_test_3					86.93 197	89.77 208	81.61 210

ambc			67.96 219		73.69 224	79.79 224	73.82 221		71.61 212	59.80 213	46.00 224	20.79 234	66.15 217	86.92 199	80.11 218	89.13 222	90.50 194

MTAPA										95.36 2		97.46 21

MTMP										95.70 1		96.90 27

Patchmatch-RL test							18.47 236

tmp_tt				50.24 223	68.55 227	46.86 232	48.90 231	18.28 230	86.51 133	68.32 180	70.19 173	65.33 191	26.69 229	74.37 222	66.80 224	70.72 230

XVS					95.68 64	98.66 15	94.96 64			88.03 55		96.06 33				98.46 34

X-MVStestdata					95.68 64	98.66 15	94.96 64			88.03 55		96.06 33				98.46 34

mPP-MVS					98.76 23							95.49 40

NP-MVS									91.63 68

Patchmtry						92.39 182	89.18 167	73.30 212		71.08 161

DeepMVS_CX						71.82 227	68.37 225	48.05 228	77.38 193	46.88 228	65.77 192	47.03 231	67.48 214	64.27 227		76.89 228	76.72 222

High-resolution multi-view benchmark